Ahmed F. Ali

Work place: Suez Canal University, Dept. of Computer Science, Faculty of Computers and Information, Ismailia, 41552, Egypt

E-mail: ahmed_fouad@ci.suez.edu.eg


Research Interests: Bioinformatics, Computational Learning Theory, Parallel Computing, Data Mining, Data Structures and Algorithms, Combinatorial Optimization


Ahmed F. Ali Received the B.Sc., M.Sc. and Ph.D. degrees in computer science from the Assiut University in 1998, 2006 and 2011, respectively. Currently, he is a Postdoctoral Fellow at Thompson Rivers University, Kamloops, BC Canada. In addition, he is an Assistant Professor at the Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt.
He served as a member of computer science department Council from 2014-2015. He worked as director of digital library unit at Suez Canal University; he is a member in SRGE (Scientific Research Group in Egypt). He also served as a technical program committee member and reviewer in worldwide conferences. Dr. Ali research has been focused on meta-heuristics and their applications, global optimization, machine learning, data mining, web mining, bioinformatics and parallel programming.

Author Articles
Genetic Local Search Algorithm with Self-Adaptive Population Resizing for Solving Global Optimization Problems

By Ahmed F. Ali

DOI: https://doi.org/10.5815/ijieeb.2014.03.08, Pub. Date: 8 Jun. 2014

In the past decades, many types of nature inspired optimization algorithms have been proposed to solve unconstrained global optimization problems. In this paper, a new hybrid algorithm is presented for solving the nonlinear unconstrained global optimization problems by combining the genetic algorithm (GA) and local search algorithm, which increase the capability of the algorithm to perform wide exploration and deep exploitation. The proposed algorithm is called a Genetic Local Search Algorithm with Self-Adaptive Population Resizing (GLSASAPR). GLSASAPR employs a self-adaptive population resizing mechanism in order to change the population size NP during the evolutionary process. Moreover, a new termination criterion has been applied in GLSASAPR, which is called population vector (PV ) in order to terminate the search instead of running the algorithm without any enhancement of the objective function values. GLSASAPR has been compared with eight relevant genetic algorithms on fifteen benchmark functions. The numerical results show that the proposed algorithm achieves good performance and it is less expensive and cheaper than the other algorithms.

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